scholarly journals Diagnosis of Alzheimer’s Disease Based on Structural MRI Images Using a Regularized Extreme Learning Machine and PCA Features

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Ramesh Kumar Lama ◽  
Jeonghwan Gwak ◽  
Jeong-Seon Park ◽  
Sang-Woong Lee
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Selection ◽  
Extreme Learning Machine ◽  
Support Vector ◽  
Complex Data ◽  
Feature Selection Technique ◽  
Brain Functions ◽  
Discriminative Approach ◽  
Learning Machine

Alzheimer’s disease (AD) is a progressive, neurodegenerative brain disorder that attacks neurotransmitters, brain cells, and nerves, affecting brain functions, memory, and behaviors and then finally causing dementia on elderly people. Despite its significance, there is currently no cure for it. However, there are medicines available on prescription that can help delay the progress of the condition. Thus, early diagnosis of AD is essential for patient care and relevant researches. Major challenges in proper diagnosis of AD using existing classification schemes are the availability of a smaller number of training samples and the larger number of possible feature representations. In this paper, we present and compare AD diagnosis approaches using structural magnetic resonance (sMR) images to discriminate AD, mild cognitive impairment (MCI), and healthy control (HC) subjects using a support vector machine (SVM), an import vector machine (IVM), and a regularized extreme learning machine (RELM). The greedy score-based feature selection technique is employed to select important feature vectors. In addition, a kernel-based discriminative approach is adopted to deal with complex data distributions. We compare the performance of these classifiers for volumetric sMR image data from Alzheimer’s disease neuroimaging initiative (ADNI) datasets. Experiments on the ADNI datasets showed that RELM with the feature selection approach can significantly improve classification accuracy of AD from MCI and HC subjects.

scholarly journals Use of a Sparse-Response Deep Belief Network and Extreme Learning Machine to Discriminate Alzheimer's Disease, Mild Cognitive Impairment, and Normal Controls Based on Amyloid PET/MRI Images

2021 ◽  
Vol 7 ◽  
Author(s):  
Ping Zhou ◽  
Shuqing Jiang ◽  
Lun Yu ◽  
Yabo Feng ◽  
Chuxin Chen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Extreme Learning Machine ◽  
Deep Belief Network ◽  
Support Vector ◽  
Belief Network ◽  
Learning Machine ◽  
Normal Controls

In recent years, interest has grown in using computer-aided diagnosis (CAD) for Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI). However, existing CAD technologies often overfit data and have poor generalizability. In this study, we proposed a sparse-response deep belief network (SR-DBN) model based on rate distortion (RD) theory and an extreme learning machine (ELM) model to distinguish AD, MCI, and normal controls (NC). We used [18F]-AV45 positron emission computed tomography (PET) and magnetic resonance imaging (MRI) images from 340 subjects enrolled in the ADNI database, including 116 AD, 82 MCI, and 142 NC subjects. The model was evaluated using five-fold cross-validation. In the whole model, fast principal component analysis (PCA) served as a dimension reduction algorithm. An SR-DBN extracted features from the images, and an ELM obtained the classification. Furthermore, to evaluate the effectiveness of our method, we performed comparative trials. In contrast experiment 1, the ELM was replaced by a support vector machine (SVM). Contrast experiment 2 adopted DBN without sparsity. Contrast experiment 3 consisted of fast PCA and an ELM. Contrast experiment 4 used a classic convolutional neural network (CNN) to classify AD. Accuracy, sensitivity, specificity, and area under the curve (AUC) were examined to validate the results. Our model achieved 91.68% accuracy, 95.47% sensitivity, 86.68% specificity, and an AUC of 0.87 separating between AD and NC groups; 87.25% accuracy, 79.74% sensitivity, 91.58% specificity, and an AUC of 0.79 separating MCI and NC groups; and 80.35% accuracy, 85.65% sensitivity, 72.98% specificity, and an AUC of 0.71 separating AD and MCI groups, which gave better classification than other models assessed.

Prediction of neuro-degenerative disorders using sunflower optimisation algorithm and Kernel extreme learning machine: A case-study with Parkinson’s and Alzheimer’s disease

2021 ◽  
pp. 095441192110609
Author(s):  
Kishore Balasubramanian ◽  
Ananthamoorthy NP ◽  
Ramya K
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Extreme Learning Machine ◽  
Feature Subset ◽  
Optimisation Algorithm ◽  
Unseen Data ◽  
Feature Extraction And Selection ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
Optimal Feature

Parkinson’s and Alzheimer’s Disease are believed to be most prevalent and common in older people. Several data-mining approaches are employed on the neuro-degenerative data in predicting the disease. A novel method has been built and developed to diagnose Alzheimer’s (AD) and Parkinson’s (PD) in early stages, which includes image acquisition, pre-processing, feature extraction and selection, followed by classification. The challenge lies in selecting the optimal feature subset for classification. In this work, the Sunflower Optimisation Algorithm (SFO) is employed to select the optimal feature set, which is then fed to the Kernel Extreme Learning Machine (KELM) for classification. The method is tested on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and local dataset for AD, the University of California, Irvine (UCI) machine learning repository and the Istanbul dataset for PD. Experimental outcomes have demonstrated a high accuracy level in both AD and PD diagnosis. For AD diagnosis, the highest classification rate is obtained for the AD versus NC classification using the ADNI dataset (99.32%) and local dataset (98.65%). For PD diagnosis, the highest accuracy of 99.52% and 99.45% is achieved on the UCI and Istanbul datasets, respectively. To show the robustness of the method, the method is compared with other similar methods of feature selection and classification with 10-fold cross-validation (CV) and with unseen data. The method proposed has an excellent prospect, bringing greater convenience to clinicians in making a better solid decision in clinical diagnosis of neuro-degenerative diseases.

An Extreme Learning Machine based Approach to Detect the Alzheimer's Disease

2021 ◽  
Author(s):  
Amrutha Hari ◽  
H R Vanamala ◽  
Sanjana N ◽  
Vijaya Krishna A ◽  
Rohan Jain
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Extreme Learning Machine ◽  
Learning Machine

An Embedded Feature Selection and Multi-Class Classification Method for Detection of the Progression from Mild Cognitive Impairment to Alzheimer's Disease

2020 ◽  
Vol 10 (2) ◽  
pp. 370-379 ◽  
Author(s):  
Jie Cai ◽  
Lingjing Hu ◽  
Zhou Liu ◽  
Ke Zhou ◽  
Huailing Zhang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Selection ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Treatment Plan ◽  
Feature Selection Method ◽  
Selection Method ◽  
Support Vector ◽  
Feature Subset

Background: Mild cognitive impairment (MCI) patients are a high-risk group for Alzheimer's disease (AD). Each year, the diagnosed of 10–15% of MCI patients are converted to AD (MCI converters, MCI_C), while some MCI patients remain relatively stable, and unconverted (MCI stable, MCI_S). MCI patients are considered the most suitable population for early intervention treatment for dementia, and magnetic resonance imaging (MRI) is clinically the most recommended means of imaging examination. Therefore, using MRI image features to reliably predict the conversion from MCI to AD can help physicians carry out an effective treatment plan for patients in advance so to prevent or slow down the development of dementia. Methods: We proposed an embedded feature selection method based on the least squares loss function and within-class scatter to select the optimal feature subset. The optimal subsets of features were used for binary classification (AD, MCI_C, MCI_S, normal control (NC) in pairs) based on a support vector machine (SVM), and the optimal 3-class features were used for 3-class classification (AD, MCI_C, MCI_S, NC in triples) based on one-versus-one SVMs (OVOSVMs). To ensure the insensitivity of the results to the random train/test division, a 10-fold cross-validation has been repeated for each classification. Results: Using our method for feature selection, only 7 features were selected from the original 90 features. With using the optimal subset in the SVM, we classified MCI_C from MCI_S with an accuracy, sensitivity, and specificity of 71.17%, 68.33% and 73.97%, respectively. In comparison, in the 3-class classification (AD vs. MCI_C vs. MCI_S) with OVOSVMs, our method selected 24 features, and the classification accuracy was 81.9%. The feature selection results were verified to be identical to the conclusions of the clinical diagnosis. Our feature selection method achieved the best performance, comparing with the existing methods using lasso and fused lasso for feature selection. Conclusion: The results of this study demonstrate the potential of the proposed approach for predicting the conversion from MCI to AD by identifying the affected brain regions undergoing this conversion.

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